System and computer-based method for simulating a human-like control behaviour in an environmental context

ABSTRACT

A computer-based method for simulating a human-like decision in an environmental context, comprising: capturing environmental data with at least one sensor, realising a computer based method for realising a bi-directional compression of high dimensional data by compressing the data into a lower-dimensional map, if environmental data are captured during a learning phase of a computer-based model, evaluate the map of compressed data by determining the quality of the map by how well it separates data with different properties, the captured data corresponding to known pre-recorded data that have been pre-evaluated, if environmental data are captured after the learning phase, add new point to the compressed data and generate a signal indicating which human-like decision to use to correspond to the state of the operator.

BACKGROUND OF THE INVENTION

The invention relates to a computer-based method for simulating ahuman-like control behaviour in an environmental context, and inparticular such a system and method including a computer-based methodfor realising a bi-directional compression of data.

Today, assisting systems are used for many applications in almost anykind of industry. Especially the automotive industry or the aviationindustry as well as in computational industry assisting systems arecommonly used to facilitate for a user the operating systems. Inautomotive industry, well known systems like ESC (Electronic StabilityControl) for improving the safety of the vehicle's stability bydetecting and minimizing of the skidding, or EBA (Emergency BrakeAssist) to ensure maximum braking power in emergency situations byinterpreting the speed and the force at which a brake pedal is pushedare widely-used. In aviation industry active autopilots or so calledfly-by-wire systems are commonly used in modern civil as well asmilitary aircrafts, where electronic interfaces replace the manualflight control by converting the movements of the flight controls intoelectronic signal and initiate flight control without humaninterference.

Additionally, the number of sensors and actuators integrated in thesystems, like vehicles or aircrafts, increases rapidly with the aim towatch over and/or control almost each and every component in thesesystems electronically.

One problem arising from the increased number of sensors and actuatorsis the amount of data to be handled and/or analysed for recognising aspecific situation or operational status of the systems.

It is an intention of modern assisting systems to provide a robust,sensitive and real-time monitoring of human intention while operating amachine in a dynamically changing environment. Such modern assistingsystems enable efficient and safe operation of a machine, optimized toactual human intended operation. Such machine may be any kind of systemto be operated by a human, like e.g. a vehicle, an aircraft, a ship, amachine etc. as well as computational systems like, e.g. a computergame, or the like. In the following, the term “system” should beunderstood as any kind of these.

Assisting systems generally use virtual development methods. Suchmethods are quickly gaining importance in the vehicle developmentprocess to cope with important business challenges; shorteningtime-to-market, optimising product quality, performance & value,reducing production & development costs, dealing with ever stricteremission & safety regulations. For example, a particular challenge inthe Engine and Powertrain field is the upcoming European “Real DrivingEmissions” (RDE) regulation. RDE compliance requires control of vehicleexhaust emissions over a wide area of operating conditions. This heavilyimpacts the engine calibration process which was traditionally based ona fixed driving cycle.

Virtualising part of the calibration process allows evaluating thevehicle & engine behaviour under various real-life driving conditions byrunning a physical or grey-box vehicle model over a virtual drivingenvironment, operated by a virtual Driver Model.

Perceptual/cognitive architectures with an artificial (i.e. implementedon a machine, or in or as a computer or in or as a tool) memory systemare used as such assisting systems. They are particularly suited forunderstanding and controlling the dynamic behaviours of a tool inresponse to the human operator, especially for actual and safeinteractions of operator and tool in real-time in a dynamically variableenvironment.

Document WO 2014 009031 relates to a method or system or architecture,in particular a perceptual/cognitive architecture with an artificialmemory system, comprising:

-   -   at least one first node being adapted to store and recall (e.g.        long) input sequences, such (e.g. long) input sequences being        modelled as an event;    -   at least one second node being adapted to extract and save        prototypes from raw data, each prototype being representative of        data chunks, characterizing real world operation (of a tool        within its environment) with features which are similar.

In the architecture disclosed in this document, the second node providesthe input for the first node. The raw data is typically data obtainedfrom sensors, e.g. sensors that sense the activity of actuators orcontrol elements of a machine or tool.

An artificial memory system is generally composed of several nodes eachof them solving a specific task like for example, one of the followingones: spatial pooling, signal quantization, temporal and forced temporalpooling, event and forced event pooling. The functionality of the wholesystem is achieved by connecting these nodes with different kind oforiented connections, like: Feed Forward (FF) input/output, Feed Back(FB) input/output, State Consistency Check, and so on.

The Memory Prediction Framework (MPF) described in this document is in aparticular embodiment an “artificial memory recalling system” inspiredby the functioning of the mammalian neocortex, able to identify, store,recognize spatial/temporal patterns of sensed parameters, able ofclassifying the events being exposed to, and predicting over time. AnMPF network is composed of a hierarchical set of nodes that can containa neural gas-like adaptive memory system, able to analyse differentspatial-temporal inputs in parallel and then communicate throughout thehierarchy in order to discover higher level behaviours and properties.

The three main purposes of a node, which has an input and an output, areto learn and recognize peculiar input patterns, associate them to anumber of external (possibly unknown) causes or events, and finallypredict the next input pattern(s).

To these aims two different phases are usually distinguished: thetraining phase, or learning phase, used to learn input patterns, whichaffects the internal “memory” of a node, and the inference phase whichis the actual operational phase for a node during which input patternsare compared against memorized ones and FF and FB outputs are generated.

Actually nothing prevents a node to operate in a “continuous learning”way in which training and inference are performed together. Anotherpossibility is to have a very short initial training phase, used toovercome the “cold start” problem, followed by a continuous learningphase.

In any case, the purpose for the inference phase is to evaluate thecurrent input pattern in order to associate it to one or more, learnedcauses. This results in the generation of a FF output signal whichrepresent the likelihood for the current input pattern to belong to oneof the learned causes. This process is usually affected also by the FBinput signal which represents a prediction for the current FF outputsignal provided by a parent node. This last feature is intended topermit a node to disambiguate among different causes which might sharecommon input patterns: this is usually referred to as “focus ofattention”, After the generation of the FF output a node should alsogenerate a prediction for its next input pattern to be signalled tochild nodes via its FB output: this might require to wait for all thenetwork to finish the FF update in order to propagate prediction fromtop to bottom nodes.

It is evident that the inter-nodes exchange of information by means ofFF and FB connections is at the core of the memory prediction framework(MPF) such as disclosed in WO 2014009031 mentioned here above.

Thus, the efficiency of communication between nodes impacts the wholesystem both in terms of the number of bits than can be exchanged, andthe computational effort within the node. Indeed, the greater theefficiency is the more bits can be exchanged between two nodes and theless computational effort within a node is needed to process a sameamount of data.

Bi-directional mapping is a technique to perform dimensionalityreduction avoiding the typical drawbacks of common techniques such asPrincipal Component Analysis (PCA) and manifold learning algorithms ingeneral. In particular, avoid linearity, which is a constraint on thelearned manifolds shape which results in maps with low accuracy, andavoid directionality, which contributes in the impossibility to recoveroriginal high dimensional points from their low dimensionalrepresentation.

In the article entitled “Visualizing Data using t-SNE” by Laurens vander Maaten and Geoffrey Hinton and published in the Journal of MachineLearning Research, volume 1 (2008) pages 1-48, is disclosed a well-knowncomputer based method named t-Distributed Stochastic Neighbour Embedding(t-SNE).

The t-SNE method allows visualizing high-dimensional datasets exploitinga dimensionality reduction which preserves proximity without anypossible inference on the visualisations.

OBJECT AND SUMMARY OF THE INVENTION

The invention aims to improve a memory prediction framework globalefficiency by performing an invertible dimensionality reduction on theinter-node communication which allows a data-dependent bi-directionalmapping between high- and low-dimensional spaces.

This goal is reached thanks to a computer based method for realising abi-directional compression of data by compressing high dimensional datainto a lower dimensional map, e.g. a two- or three-dimensional map, themethod comprising a reception of data with a first dimension greaterthan one, for example data captured and transmitted by at least onesensor, and a compression of said data or attributes derived from saiddata, such as segments of GPS data or segments of data of the on-boardelectronic control unit of a vehicle, into compressed data with a seconddimension lower than the first dimension.

According to a general feature of this computer based method, for twometric spaces X=(x,μ_(x)) and Y=(y,μ_(y)) with x a first space whichdimension is greater or equal than the dimension of a second space y,μ_(x) a first metric on said first space x, μ_(y) a metric on saidsecond space y, and P being a first set of n points belonging to saidfirst space x, a point being an element of a space, the compression isrealized by finding a second set of n points Q belonging to said secondspace y such that:

Q=argmin_(tϵy) _(n) d(A ₁(μ_(x)(P)),A ₂(t)))  (Equation 1)

where d(.,.) is a symmetric distance function on the space of squarematrices, while A₁ and A₂ are any two functions holding the followingproperties, A being any of A₁ or A₂ functions:

A: R_(4→)[0, 1],

A(0)=1,

lim_(x→∞)A(z)=0,

A(z₁)>A(z₂) if and only if z₁<z₂.

A₁ and A₂ can be any function holding said properties, including:

${A(z)} = e^{- \frac{z}{s}}$

with s>0, or

${{A(z)} = e^{- {(\frac{z}{s})}^{2}}},$

with s>0, or

${A(z)} = \frac{1}{1 + ( \frac{z}{s} )^{2}}$

with s>0, or

${A(z)} = \frac{1}{1 + ( \frac{z}{s} )}$

with s>0.

Once equation 1 is solved for each of the n points of the first set of npoints P, correspondence between points, referenced as BiMap, of the twoset of n points P and Q, ice, BiMap=(P,Q), can be used as a translationatlas to transform points of said first space x into points of saidsecond space y and vice-versa by means of equations 2 and equation 3which follow.

First and second spaces x and y are general spaces where dimension of yis smaller than dimension of x. The computer-based method creates acorrespondence between points in x and points in y. Thus the compressionis achieved by having the points of y in a smaller dimension than thepoints of x. This compression method is defined so as to bebi-directional as it allows both compression and decompression of data,in contrast to the other manifold learning algorithms which allow onlycompression and not decompression of data.

The two main difference between this computer based method and the t-SNEcomputer based method consists in the possibility given here to both addnew point to a map without the need to re-compute the whole map, andcreate an inverse correspondence from matrix y to matrix x to recover ahigher dimensional point from its lower dimensional representation.

Indeed, t-SNE does not create any mapping and any addition of a newpoint constrains to re-compute all the data to visualize ft.

The second difference with t-SNE is also the main difference with thestandard manifold learning computer based methods as disclosed in thearticle entitled “Iterative non-linear dimensionality reduction withmanifold sculpting” from Gashler et. al. and published in NIPS (2007),volume 8, pages 513-520, and in the article entitled “Diffusion maps”from Coffman et al and published in the magazine Applied andcomputational harmonic analysis, volume 21, issue 1, July 2006, pages5-30.

Moreover, depending on the choice of functions A₁ and A₂ the computerbased method can accurately transport the proximity relation from P toQ, and/or can show self-clustering features, and/or help to highlighthidden patterns in data. For example if A1 and A2 are the same functionthen the proximity relation is accurately transported, while whenever A2has a fatter tails than A1, then points which are not so close on x willbe spread away on the projection over y, thus highlighting theneighborhood structure, i.e. the clustering, of the original points inx.

In a first aspect of this computer based method, it further comprises anextraction of attributes from at least one data before initiating saidbi-directional compression.

The extraction of attributes from each data enables to already sort outthe elements of each data considered as relevant for future operationsand thus already realise a first compression of used data by onlykeeping the relevant parts, i.e. attributes, for the bi-directionalcompression into two- or three-dimensional maps.

Moreover, the extraction of attributes such as spatiotemporal attributeshelps simplifying the training of a computer-based model using thebi-directional mapping compression.

In a second aspect of this computer based method, said compressionmethod comprises a compression of a new point m belonging to matrix x onmatrix y by solving the following equation:

{tilde over (y)}=argmin_(tϵy) _(n) d(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 2)

and a decompression of a new point t belonging to matrix y on matrix xby solving the following equation:

{tilde over (x)}=argmin_(mϵx) _(n) d(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 3)

where μ_(x)(P,m) and μ_(y)(Q,t) are the distance vectors between thepoints of BiMap and the new points in the corresponding respectivespaces, BiMap is a correspondence between points of x and points of ygiven by Equation 2 (compressing) and equation (3) decompressing.

Moreover A₁(μ_(x)(P,m)) or A₂(μ_(y)(Q,t)) can be used to assess anobjective Map Distortion Index (MDI) when a new point m or t needs to bemapped in Y or X respectively. Indeed, the mapping accuracy obtained bythis computer-based method depends on the closeness with respect to thepoints in the atlas BiMap=(P,Q). The dependence is expressed bysearching the point which has minimum activation distortion from thelearned ones, i.e. solving equation 2 or equation 3. The possibility toassess an MDI leads to both a point section scheme in the X space inorder to have more accurate atlas and to an intrinsic anomaly detectionmechanism to support or to implement the context check within the MPF.

Another object of the invention is to provide a computer-based methodfor simulating a human-like decision in an environmental context. Themethod comprises the following steps:

-   -   capturing environmental data with at least one sensor,    -   realising a computer based method for realising a bi-directional        compression of data by compressing high dimensional        environmental data into a lower dimensional map as defined        above,    -   if environmental data are captured during a learning phase of a        computer-based model, evaluating the map of compressed data by        determining the quality of the map by how well it separates data        with different properties, said captured data corresponding to        known pre-recorded data that have been pre-evaluated,    -   if environmental data are captured after said learning phase,        add new points to said compressed data and generate a signal        indicating which human-like decision to use to correspond to the        state of the operator.

Depending on the environmental context and the use of the method, thehuman-like decisions simulated can be human judgements about theirenvironments taking into consideration some first type of environmentaldata like sounds for example, or human-like control behaviour controlfor example to predict what kind of action a human, a driver e.g., isgoing to take knowing some second type of environmental data like theposition, the speed and the gear of the car, or other kinds ofhuman-like decisions.

Thus, in an architecture comprising a memory prediction framework, thiscomputer-based method can act in three different ways: First as an inputdata pre-processing, second as an internode feedforward and feedbackdata compression, and third as a node output classifier.

In the first configuration, i.e. the input data pre-processing, the roleplayed by the bi-directional mapping computer-based compression methodis to let the network process data from a domain with less dimensions,thus saving both memory and computational power.

In the second configuration, i.e. the internode feedforward and feedbackdata compression, the bi-directional mapping computer-based compressionmethod is used to compress feedforward outputting from the lowest levelnode and decompress the feedback outputting from the highest level one.

In the third configuration, i.e. the node output classifier, thebi-directional mapping computer-based compression method helps tovisualize the learning dynamics within a node, thus to help the humanqualitative interpretation of the information stored in it. In this casethe compression method acts as a clustering based classifier.

In the second configuration, during inference tasks which happen afterthe end of the learning phase, according to WO 2014 009031 an MPF nodecomputes the distance, with respect to a defined metric, between thecurrent input and all its stored coincidences, hence it outputs thefeedforward signal consisting in a distance related probabilitydistribution of the input being a sample of the known storedcoincidences.

The space of coincidence, within a node, is coupled with a metricimplying both that the coincidences have a naturally defined spatialnotion within the node input space and that the feedforward signal is acodification of the input in terms of its position within the inputspace by using the coincidences as reference points.

In other words, for any new input M the output of an MPF node N is thevector A_(N)(μ_(N)(C,m)), with A_(N)(.) the activation function of thenode N, μ_(N)(.) its metric, and C the set of its coincidences.

Thus the output of any node is the first half of equation 2 andsymmetrically a feedback output is the second part of equation 3.

In particular, the dimensionality of the output of each node increaseswith the memory size of the node itself. During real-world usage of theMPF, i.e. during inference phase, it is possible to have nodes withoutput dimensionality of hundreds or thousands of probabilities. Theneed for dimensionality reduction is important when stacking nodes in ahierarchy, like the MPF, since the nodes in the above levels must learnfrom the output of the nodes in the lower ones.

In particular a smaller dimension input means reduced computationalpower needed to perform all the MPF capabilities.

This problem is even more relevant when a higher level node receives asinput the output of several lower level ones, then the dimension of itsinput space grows accordingly to the sum of the number of coincidencesof its input nodes.

The need of back projection in feedback connections clearly follows,since it is mandatory for many MPF features.

Moreover, whenever the order of C exceeds by at least one the number ofdimension of the input space N, there is a one to one correspondencebetween the position of the input m within the node input space and thespace of the feedforward signals in output, meaning that any furthercorrespondence add redundancy to A_(N)(μ_(N)(C,m)) which can be clearedout by said compression method.

Nowadays, the sound quality of a car is a very important aspectconsidered by a consumer, for example when buying a new car. Since thesound quality is mostly a subjective aspect, there are no reliable waysto objectively evaluate the quality. Therefore the evaluation stepduring the car development usually relies on judgments given by experts,often on a relative scale between different cars.

Thus, this aspect of this computer-based method enables to objectify thesubjective judgments of car sounds, in other words it enables toobjectify the process of evaluating car sounds.

In a first aspect of the computer-based method for simulating ahuman-like decision in an environmental context, said computer-basedmethod comprises pre-processing said environmental data with at leastone filter before realising the computer based method for realising abi-directional compression of data, said computer based method forrealising a bi-directional compression of data receiving as inputfiltered environmental data.

This additional step enables, especially in an application to car audiosound treatment, the environmental data to go through a series offilters, such as band-pass filters, and pre-processing steps which helpsincreasing and diversifying the amount of information treated. Forinstance, in an application to car audio sound treatment, pre-processingsteps can include denoising, convolution or any possible audioprocessing. In general it can be any kind of data operation or featureextraction.

An MPF architecture is mostly un-supervised and data-driven. Therefore,filtering out data may help in excluding useless or misleadinginformation, helping MPF to focus on more important features.

In a second aspect of the computer-based method for simulating ahuman-like decision in an environmental context, the environmental datacomprise at least one sample of raw sound recorded inside a car'scockpit with a noise sensor, and said method further comprises inputtingsaid environmental data into auditory models before pre-processing withat least one filter the data outputted by said auditory models todeliver a plurality of different versions of the environmental data,said pre-processing being realized over every version of theenvironmental data.

Thus, this aspect of this computer-based method enables to objectify thesubjective judgments of car sounds, in other words it enables toobjectify the process of evaluating car sounds.

The raw sound can be recorded by using high fidelity microphonessynchronized with the CAN bus data, i.e. the controller area network.

Auditory models are bio-inspired techniques to transform acoustic datainto psycho-acoustic data. The correspondences between physical soundproperties and human perception are complicated and far from beinglinear. Several operations are performed such as integration, temporaland frequency masking. From a computational point of view, an auditorymodel can be seen as a bank of filters whose task is to reproduce theoperations performed by the human ear. This approach is widely used inspeech recognition, but rarely considered in the automotive field.

The raw recorded sound is processed using the auditory models presentedin scientific literature. The output of this step is the amount ofenergy in a set of determined frequency bands, considering the effectsof the different auditory models, such as frequency selection, frequencyand temporal masking.

A set of different Auditory Models are used to generate a plurality of“psychoacoustic versions” of the same raw sound to be processed in thefollowing steps.

Another object of the invention is to propose a system for simulating ahuman-like behaviour in an environmental context comprising at least onesensor to capture environmental data, and an architecture with a memoryfor interacting with dynamic behaviours of a tool and an operator.

According to a general feature of the system for simulating a human-likebehaviour in an environmental context, the architecture are configuredto process the steps of said computer-based method for simulating ahuman-like decision in an environmental context defined here above.

Moreover, all the features in application WO 2014 009031 are integratedin the present specification.

In a first aspect of the system for simulating a human-like behaviour inan environmental context, the memory of the architecture is anartificial memory, the architecture being a first neural network havingstructures and mechanisms for abstraction, generalisation and learning,the first neural network implementation comprising an artificialhierarchical memory system, comprising a receiving port configured toreceive data generated by sensors (on the tool), one or more first nodesconfigured to learn and recognize frequently occurring input attributesand sequences received from the receiving port, said one or more firstnodes forming a second neural network comprising neurons as componentsand edges connecting two or more of said neurons in a graph, and anoutput port configured to output data constructed by the architectureand associated with the behaviours, whereby each of the one or more oressentially all of said first nodes is adapted for time series analysis,and comprises components connected in a topological graph or temporalgraph.

In a second aspect of the system for simulating a human-like behaviourin an environmental context, it comprises a pre-processing filter toapply a set of different filters to the environmental data captured bythe at least one sensor.

In a third aspect of the system for simulating a human-like behaviour inan environmental context, the environmental data comprise at least onesample of raw sound recorded inside a cares cockpit with a noise sensor,and said system further comprises auditory models configured to beapplied to said recorded environmental data before the filteringpre-processing to output a plurality of different versions of theenvironmental data, said filtering pre-processing being realized overevery version of the environmental data.

Another object of the invention is to propose an automotive vehiclecomprising an electronic control unit including a system for simulatinga humanlike behaviour in an environmental context.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be better understood by reading here after, asexamples and in a non-limitative way, in reference to the encloseddrawings on which:

FIG. 1 shows schematically an artificial memory system according to anembodiment of the present invention;

FIG. 2 shows a diagram of a computer-based method for simulating ahuman-like behaviour in an environmental context.

DETAILED DESCRIPTION OF THE, EMBODIMENTS

The present invention will be described with respect to particularembodiments and with reference to certain drawings but the invention isnot limited thereto but only by the claims. The drawings described areonly schematic and are non-limiting. In the drawings, the size of someof the elements may be exaggerated and not drawn on scale forillustrative purposes. Where the term “comprising” is used in thepresent description and claims, it does not exclude other elements orsteps. Where an indefinite or definite article is used when referring toa singular noun e.g. “a” or “an”, “the”, this includes a plural of thatnoun unless something else is specifically stated.

The term “comprising”, used in the claims, should not be interpreted asbeing restricted to the means listed thereafter; it does not excludeother elements or steps. Thus, the scope of the expression “a devicecomprising means A and B” should not be limited to devices consistingonly of components A and B. It means that with respect to the presentinvention, the only relevant components of the device are A and B.

Furthermore, the terms first, second, third and the like in thedescription and in the claims, are used for distinguishing betweensimilar elements and not necessarily for describing a sequential orchronological order. It is to be understood that the terms so used areinterchangeable under appropriate circumstances and that the embodimentsof the invention described herein are capable of operation in othersequences than described or illustrated herein.

In FIG. 1 is schematically presented a system 100 for simulating ahuman-like behaviour in an environmental context according to anembodiment of the invention.

The simulation system 100 comprises two sensors 110 configured tocapture environmental data. In this embodiment, the sensors 110 are twohigh fidelity microphones placed in two different locations inside acockpit of a car.

The simulation system further comprises an architecture with anartificial memory system 120 configured to interact with dynamicbehaviours of a tool and an operator. The architecture with anartificial memory system 120 is made of a first neural network havingstructures and mechanisms for abstraction, generalisation and learning.

The first neural network implementation comprises an artificialhierarchical memory system 130 comprising a receiving port configured toreceive data generated by the microphones 110, one or more first nodes140 configured to learn and recognize frequently occurring inputattributes and sequences received from the receiving port, and one ormore second nodes 150. The one or more first nodes 140 form a secondneural network comprising neurons as components and edges connecting twoor more of said neurons in a graph, and an output port configured tooutput data constructed by the architecture and associated with thebehaviours, whereby each of the one or more or essentially all of saidfirst nodes is adapted for time series analysis, and comprisescomponents connected in a topological graph or temporal graph.

In embodiments of the present invention a human operator is mentionedsuch as a driver, captain, pilot, gamer. Such a person can be“autonomous”. For example the operator commands can be given a priori,in the form of commands stored on a suitable storage medium that can beread by the system. For example if the operator is a driver and the toolis a vehicle such as an automobile, the commands might be destinations,expected times of arrival, expected fuel consumption, whether to driveaggressively, sportily, calmly etc. The actual human driver can alsointentionally switch on the system and a command can be provided todrive the driver to the destination in such a way that accelerations,braking force, speed, fuel consumption etc. are according to thedriver's normal behaviour or other behaviour. Also the system can takeover in case of lack of commands. For example if it is detected that thedriver is asleep the system brings the vehicle safely to a stand-still.Whether the driver has fallen asleep can be detected by an AwarenessCheck.

Embodiments of the present invention are based on the Memory PredictionFramework (MPF) and include an artificial hierarchical memory system.The artificial hierarchical memory system can be enhanced HierarchicalTemporal Memory (HIM) with improved capabilities for scenerecognition/“classification” and/or a pre-processing stage. The systemand method have been adapted to be able among others, to provide anumber such as 10-15 samples or any suitable number of samples required,for e.g. more or less than half a second depending on the samplingfrequency, a prediction about input operator commands due to theintroduction of temporal events management as well as theabove-mentioned pre-processing stage.

The architecture of the embodiment of FIG. 1 corresponds to thearchitecture described in document WO 2014 009031 comprising an enhancedhierarchical memory.

The Memory Prediction Framework is appropriate as a fundamental model,as it foresees nearly all the structures and mechanisms necessary forgood learning, abstraction and generalization, e.g. feed-forward andfeed-back signal flows within the network, their correspondence asmeasure of the degree of understanding of the situation, which is aprerequisite for assessing the generalization capabilities of thenetwork. The MPF computational approach and its learning algorithms havebeen proven to deal well with diverse sensory channels, althoughsensory-specific pre-processing or tuning is still required. Once setup,i.e. adapted to a particular sensor modality no pre-processing is inprinciple required. However, it may from time to time be applied withadvantage, for example as previously mentioned so-called primitives.Embodiments of the present invention may be seen as instantiations ofsuitable parts of the MPF.

FIG. 2 schematically presents a computer-based method for simulating ahuman-like decision in an environmental context. This simulation methodis realised by the nodes 140 and 150 inside the artificial hierarchicalmemory 130 of the simulation system 100.

In this example, the human-like decision is a behaviour of a driver of acar and the environmental context taken into account corresponds to thesounds surrounding him.

In a first step 10 of the simulation method, several high fidelitymicrophones synchronized with the CAN bus data record raw sounds insidethe cockpit of the car. The raw sounds recorded are in a highdimensional configuration, for example a three dimension configuration.

In a second step 12 of the simulation method, the electronic signalscorresponding to the recorded raw sounds are sent to the architecturewith said artificial memory system 120 and are pre-processed through aset of auditory models to generate a plurality of “psychoacousticversions” of each recorded raw sound which will be inputted into theartificial memory system 120 after a filtering step.

In a third step 14 of the simulation method, the electronic signalscorresponding to the multiple psychoacoustic versions of each recordedraw sound are pre-processed through a set of band-pass filters.

In a following step 16 of the simulation method, the filtered sounds aresent to nodes to be processed by using a computer-based method forrealising a bi-directional compression of data by compressing saidfiltered data into a two-dimensional map.

In step 18, a following step of the simulation method and first step ofthe compression method, the filtered sounds are received by other nodesor the same nodes.

In a second step 20 of the compression method, the nodes operate anextraction of attributes from each filtered raw sound in order to sortout the elements of each data considered as relevant for the method forsimulating a human-like control behaviour.

The attributes extracted are e.g. spatiotemporal attributes in order tosimplify the training of the computer-based method for simulating ahuman-like control behaviour.

In a third step 22 of the compression method, the nodes operate acompression of the attributes extracted from the filtered raw sounds.

The compression is realized by finding a set of n points Q belonging toa matrix y such that:

Q=arginin_(tϵy) _(n) d(A ₁(μ_(x)(P)),A ₂(μ_(y)(t)))  (Equation 1)

X=(x,μ_(x)) and Y=(y,μ_(y)) being a two metric spaces with x a firstspace which dimension is greater or equal than the dimension of a secondspace y, μ_(x) a first metric on space x, μ_(y) a second metric on spacey, and P being a first set of n points belonging to said first space x,and d(.,.) being a symmetric distance function on the space of squarematrices, while A₁ and A₂ are any two functions holding the followingproperties:

-   -   A:R₃₀ ^(→)[0, 1], i.e. A being a function defined for all        positive real numbers and generating a value between 0 and 1, 0        and 1 being included;    -   A(0)=1, i.e. A being a function for which argument 0 will        generate the value 1,    -   lim_(x→∞)A(z)=0, i.e. A being a function which has 0 for a limit        as the argument approaches grows,    -   A(z₁)>A(z₂) if and only if z₁<z₂, i.e. A being a strictly        decreasing function.

Once equation 1 is solved for each of the n points of the first set of npoints P, the correspondence between points, referenced as BiMap, of thetwo set of n points P and Q, i.e. BiMap=(P,Q), can be used as atranslation atlas to transform points of said first space x into pointsof said second space y and vice-versa by means of equations 2 andequation 3, which follow.

With such a step, new points can thus be added to a map without the needto re-compute the whole map, and an inverse correspondence from space yto space x is created to recover a higher dimensional point from itslower dimensional representation.

Hence, any new point in belonging to space x can be compressed on spacey by solving the following equation:

{tilde over (y)}=argmin_(tϵy) _(n) ^(d)(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 2)

and any new point t belonging to space y can be decompressed on space xby solving the following equation:

{tilde over (x)}=argmin_(mϵx) _(n) d(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 3)

where μ_(x)(P,m) and μ_(y)(Q,t) are the distance vectors between thepoints of BiMap and the new points in the corresponding respectivespaces, BiMap being a pair of points, BiMap=(P,Q), solving equation 1.

Depending on the phase in the nodes 140 and/or 150 are, the compressionmethod can act in three different ways.

In a learning phase, it can act as an input data pre-processing to letthe network process data from a domain with less dimensions, thus savingboth memory and computational power.

In an inference phase, after the learning phase, it can act either as aninternode feedforward (FF) and feedback (FB) data compression or as anode output classifier.

When acting as an internode FF and FB data compression, the compressionmethod is used to compress FF outputting from a lower level node anddecompress the feedback outputting from a higher level one.

When acting as a node output classifier, the compression method helps tovisualize the learning dynamics within a node, thus to help the humanqualitative interpretation of the information stored in it. In thiscase, the compression method acts as a clustering based classifier.

If the architecture is in a learning phase, in a following step 24 ofthe computer-based method for simulating a human-like decision in anenvironmental context, the map of compressed data is evaluated bycomparing said compressed data with different references previouslymemorized and tagging said map of compressed data with a labelassociated to one of the references.

If the architecture in an inference phase, instead of step 24, any newattribute extracted from a derivation of a new recorded raw sound isadded in a step 26 to the map of compressed data and a signal isgenerated indicating which human-like decision to use to correspond tothe state of the human using a system using the computer-based methodfor simulating a human-like decision in an environmental context.

Thus, during inference phase, the method for simulating a human-likedecision can enable the architecture to perform several operations suchas:

evaluate a new sound, by going through the process hereby describedwithout modifying the learned memories;

infer common properties for sound that obtain similar judgments byanalysing the low dimensional clustering performed by the BiMap;

improve the reliability of human judgments, by highlighting unusualbehaviours on the maps;

generate sounds from a desired judgment; since all steps are reversible,new sounds with the expected judgments can be synthesized starting fromtheir positions on the generated BiMap.

Hereafter is another example of a use of the invention which explains ina few more details certain aspects of the invention.

Virtual development methods are quickly gaining importance in thevehicle development process to cope with important business challenges:shortening time-to-market, optimising product quality, performance &value, reducing production & development costs, dealing with everstricter emission & safety regulations.

A particular challenge in the Engine and Powertrain field is theupcoming European “Real Driving Emissions” (RDE) regulation. RDEcompliance requires control of vehicle exhaust emissions over a widearea of operating conditions. This heavily impacts the enginecalibration process which was traditionally based on a fixed drivingcycle.

Virtualising part of the calibration process allows evaluation ofvehicle & engine behaviour under various real-life driving conditions byrunning a physical or grey-box vehicle model over a virtual drivingenvironment, operated by a virtual Driver Model.

The invention is used in this example to develop a new external DriverModel, i.e. a method for simulating a human-like control behaviour, thathandles acceleration/brake, clutch and gearshift in a human-like way.

The methods of said invention are based on machine learning as it allowsthe model to learn from recorded data and to replicate driver'sbehaviour over any arbitrary route, making it suitable for RDEsimulation.

The proposed method is composed of three main phases during which thenodes of the architecture proceed to build a relation between recordeddata from tests and the simulation environment.

In a first phase, referred to as an extraction phase, the data capturedby sensors, such as sensors 110, are sent to a Map Attribute Extractor.The map attribute extractor can either have as input the recordedelectronic control unit (ECU) data along with the global positioningsystem (GPS) position or only GPS points to produce the attributes ofthe route connecting them. The first case prepares the input in order tolearn a real driver's behaviour from data while the second one isnecessary to extrapolate the learned behaviour over new routes.

In a second phase, referred to as a learning phase, captured data areinputted into a model to learn the real driver behaviour on the drivenroute and generalize it in order to replicate the behaviour over newroutes. At this stage BiMap are used to learn real driver behaviour inthe environment by sensing ECU and GPS data. In an inference stage BiMapgenerate the expected human behaviour in new environments.

In a third module, referred to as an inference phase, a FunctionalMock-up Unit (FMU) controller cooperating in closed loop with thesimulation environment uses the model build during the learning phase toimplement the driver longitudinal behaviour by acting, in a humanfeasible way, on the car controls (gear-shift and, pedals).

This example exhibits an artificial intelligence driver, which learnsreal driver behaviour on recorded routes and generalize it on arbitraryroutes. The driver model exhibits human-like driving behaviour both onvehicle pedals and gear selection, thus it is suitable to be used insimulating emissions.

The present invention may be realized on a processor system. Theprocessing system may include a computing device or processing engine,e.g. a microprocessor or an FPGA. Any of the methods described aboveaccording to embodiments of the present invention or claimed may beimplemented in a processing system. Such a processing system can includeat least one customizable or programmable processor coupled to a memorysubsystem that includes at least one form of memory, e.g., RAM, ROM, andso forth. It is to be noted that the processor or processors may be ageneral purpose, or a special purpose processor, and may be forinclusion in a device, e.g. a chip that has other components thatperform other functions. Thus, one or more aspects of the methodaccording to embodiments of the present invention can be implemented indigital electronic circuitry, or in computer hardware, firmware,software, or in combinations of them. The processing system may includea storage subsystem that has at least one disk drive and/or CD-ROM driveand/or DVD drive. In some implementations, a display system, a keyboard,and a pointing device may be included as part of a user interfacesubsystem to provide for a user to manually input information, such asparameter values. More elements such as network connections, interfacesto various devices, and so forth that allow connection to differenttypes of physical sensors may be included. The various elements of theprocessing system may be coupled in various ways, including via a bussubsystem, for simplicity as a single bus, but which will be understoodto those in the art to include a system of at least one bus. The memoryof the memory subsystem may at some time hold part or all of a set ofinstructions that when executed on the processing system implement thesteps of the method embodiments described herein.

The present invention also includes a computer program product whichprovides the functionality of any of the methods according to thepresent invention when executed on a computing device, e.g. on aprocessor of the kind described above. Software according to the presentinvention, when executed on a processing engine, can contain codesegments that provide a Perceptual/Cognitive architecture withartificial memory for interacting with dynamic behaviours of a tool andan operator, wherein Perceptual/Cognitive architecture is based on aMemory Prediction Framework implementation having structures andmechanisms for abstraction and generalization and optionally learning,the Memory Prediction Framework implementation comprising an enhancedartificial hierarchical memory system. The software may be adapted toco-operate with a pre-processing stage. The software, when executed, maybe adapted to provide a prediction about input operator commands basedon temporal events management and to describe input data from physicalsensors in terms of primitives and recurrent patterns.

The present invention also includes a computer program product havingsoftware according to the present invention, which when executed on aprocessing engine, can contain code segments that provide aPerceptual/Cognitive architecture with artificial memory for interactingwith dynamic behaviours of a tool and an operator, wherein thePerceptual/Cognitive architecture is based on a Memory PredictionFramework implementation having structures and mechanisms forabstraction and generalization and optionally learning, the memoryPrediction Framework implementation comprising an enhanced artificialhierarchical memory system. The software may be adapted to cooperatewith a pre-processing stage, the software being adapted to describeinput data from physical sensors in terms of primitives and recurrentpatterns. The software may be adapted to allow feed-forward andfeed-back signal flows and to develop the correspondence between thefeed-forward and feed-back signal flows as a measure of a context check.

The present invention also includes a computer program product whichincludes software according to the present invention, which can containcode segments that provide A Perceptual/Cognitive architecture withartificial memory for interacting with dynamic behaviours of a tool andan operator when executed on a processing engine. The architecture canbe based on a Memory Prediction Framework implementation havingstructures and the software may be adapted to provide mechanisms forabstraction and generalization and optionally learning, the memoryPrediction Framework implementation comprising an enhanced artificialhierarchical memory system. The software may be adapted to cooperatewith a pre-processing stage, the software being adapted to describeinput data from physical sensors in terms of primitives and recurrentpatterns, the software being adapted to output a compressed higher levelinformation on the behaviour of the tool and/or operator based on inputsfrom the physical sensors.

The above software may be adapted for use with a Physical I/O Layer, anEnhanced hierarchical Memory, a Time Master, a Context Check, aSupervised Gating, a System Control Unit, and a User Control I/O Layer.The software can be adapted to translate physical sensor inputs withoutmodifying the information content to a format manageable by the Enhancedhierarchical Memory. The software can be adapted to be taught and torecognize, and predict sequences of input patterns received from thePhysical I/O Layer. The software can be adapted to output a compressedhigher level information based on the physical sensor inputs received bythe Physical I/O Layer.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive; theinvention is not limited to the disclosed embodiments.

Other variations to the disclosed embodiments can be understood andeffected by those skilled in the art in practising the claimedinvention, from a study of the drawings, the disclosure, and theappended claims.

For example, a single unit may fulfil the functions of several itemsrecited in the claims. Any reference signs in the claims should not beconstrued as limiting the scope.

For example in embodiments of the present invention modelling actionscan be as “paths” or “sequences” employing Markov chains, whereby suchembodiments of HTMs can “follow” just one path.

The invention further provides for an architecture, being developedadhering to the Memory Prediction Framework (MPF) theory, in particulara system, based on the hMPF, developed to tackle a particularclassification/prediction problem, having as its main processing elementa artificial brain is provided.

The artificial brain, in turn, might comprise a number of processingnodes organized in layers and communicating each other by means offeed-forward (FF), feed-back (FB), and prediction signals. These signalsare essentially Probability Density Functions (PDFs) expressing thelikelihood for a particular input pattern to be a consequence of aparticular world cause. The whole system might further check the statusof the hierarchical network by means of context check signals alsoprovided as outputs to the network.

1. A computer based method for realising a bi-directional compression ofhigh dimensional data by compressing the data into a lower dimensionalmap, the method comprising a reception of data with a first dimensiongreater than one captured and transmitted by at least one sensor and acompression of the data or attributes derived from the data intocompressed data with a second dimension lower than the first dimension,wherein, for two metric spaces X=(x,μ_(x)) and Y=(y,μ_(y)) with x afirst space which dimension is greater or equal than the dimension of asecond space y, μ_(x) a first metric on the first space x, μ_(y) asecond metric on the second space y, the compression is realized bymapping pairs of points formed between a first set of n points Pbelonging to the first space x and a second set of n points Q belongingto the second space y, each point of the second set of n points Q beingpaired with a point of the first set of points P through the followingequation:Q=argmin_(tϵy) _(n) d(A ₁(μ_(x)(P)),A ₂(μ_(y)(t)))  (Equation 1) whereμ_(x)(P) is a first symmetric distance matrix between points in P,μ_(x)(Q) is a second symmetric distance matrix between points in Q, andd(.,.) is a symmetric distance function on the space of square matrices,while A₁ and A₂ are any two functions holding the following properties:A:R₊ ^(→)[0, 1], A(0)=1, lim_(x→∞)A(z)=0, A(z₁)>A(z₂) if and only ifz₁<z₂.
 2. The computer based method of claim 1, further comprising anextraction of attributes from at least one data before initiating thebi-directional compression.
 3. The computer based method according toclaim 1, comprising a compression of a new point m belonging to thefirst space x on the second space y by solving the following equation:{tilde over (y)}=argmin_(tϵy) _(n) d(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 2) and a decompression of a new point tbelonging to the second space y on the first space x by solving thefollowing equation:{tilde over (x)}=argmin_(mϵx) _(n) d(A ₁(μ_(x)(P,m)),A₂(μ_(y)(Q,t)))  (Equation 3) where μ_(x)(P,m) and μ_(y)(Q,t) are thedistance vectors between the points of BiMap and the new points in thecorresponding respective spaces, BiMap being a correspondence betweenpoints of the first space x and points of the second space y representedby equation 2 and equation 3, which Q points are calculated from pointsP by solving equation
 1. 4. A computer based method for simulating ahuman-like decision in an environmental context, comprising: capturingenvironmental data with at least one sensor, realising a computer basedmethod for realising a bi-directional compression of high dimensionaldata by compressing the data into lower dimensional map according toclaim 1 with the environmental data, if environmental data are capturedduring a learning phase of a computer-based model, evaluating the map ofcompressed data by determining the quality of the map by how well itseparates data with different properties, the captured datacorresponding to known pre-recorded data that have been pre-evaluated,if environmental data are captured after the learning phase, adding newpoints to the compressed data and generating a signal indicating whichhuman-like decision to use to correspond to the state of the operator.5. The computer based method according to claim 4, comprisingpre-processing the environmental data with at least one filter beforerealising the computer based method for realising a bi-directionalcompression of data, the computer based method for realising abi-directional compression of data receiving as inputs filteredenvironmental data.
 6. The computer based method according to claim 4,wherein the environmental data comprise at least one sample of raw soundrecorded inside a car's cockpit with at least one microphone, and themethod further comprises inputting the environmental data into auditorymodels before pre-processing with at least one filter the data outputtedby the auditory models deliver a plurality of different versions of theenvironmental data, the pre-processing being realized over every versionof the environmental data.
 7. A system for simulating a human-likebehaviour in an environmental context comprising at least one sensor tocapture environmental data, and an architecture with a memory forinteracting with dynamic behaviours of a tool and an operator,characterized in that architecture is configured to process the steps ofthe computer-based method for simulating a human-like decision in anenvironmental context according to claim
 4. 8. The system according toclaim 7, wherein the memory of the architecture is an artificial memory,the architecture being a first neural network having structures andmechanisms for abstraction, generalisation and learning, the firstneural network implementation defining the architecture comprising anartificial hierarchical memory system, comprising a receiving portconfigured to receive data generated by sensors (on the tool), one ormore first nodes configured to learn and recognize frequently occurringinput attributes and sequences received from the receiving port, the oneor more first nodes forming a second neural network comprising neuronsas components and edges connecting two or more of the neurons in agraph, and an output port configured to output data constructed by thearchitecture and associated with the behaviours, whereby each of the oneor more or essentially all of the first nodes is adapted for time seriesanalysis, and comprises components connected in a topological graph ortemporal graph.
 9. The system according to claim 7, further comprising apre-processing filter to apply a set of different filters to theenvironmental data captured by the at least one sensor.
 10. The systemaccording to claim 7, wherein the environmental data comprise at leastone sample of raw sound recorded inside a car's cockpit with a noisesensor, and the system further comprises auditory models configured tobe applied to the recorded environmental data before the filteringpre-processing to output a plurality of different versions of theenvironmental data, the filtering pre-processing being realized overevery version of the environmental data.
 11. A computer program producthaving software to realize the computer based method according to claim4, which when executed on a processing engine, can contain code segmentsthat provide a Perceptual/Cognitive architecture with artificial memoryfor interacting with dynamic behaviours of a tool and an operator,wherein the Perceptual/Cognitive architecture is based on a MemoryPrediction Framework implementation having structures and mechanisms forabstraction and generalization and optionally learning, the memoryPrediction Framework implementation comprising an enhanced artificialhierarchical memory system.
 12. An automotive vehicle comprising anelectronic control unit including a system for simulating a human-likedecision in an environmental context according to claim 7.